汇报标题 (Title)：Adaptive Dimension Reduction for Overlapping Group Sparsity（沉叠组稀少性的自适应维数约简）
汇报人 (Speaker)：梁经纬 副教授（上海交通大学）
汇报功夫 (Time)：2025年12月11日（周五）9:30
汇报地址 (Place)：校本部GJ303
约请人(Inviter)：周安娃
主办部门：理学院数学系
汇报提要：Typical dimension reduction techniques for sparse optimization involve screening strategies based on a dual certificate derived from the first-order optimality condition, approximating the gradients or exploiting some inherent low dimensional structure that an optimization algorithm promotes. Screening rules for overlapping group lasso are generally less developed because the subgradient structure is more complex and the link between sparsity pattern and the dual vector is generally indirect. In this talk, I will present a new strategy for certifying the support of the overlapping group lasso and demonstrate how this can be applied significantly accelerate the performance of numerical methods.
